.png){kind=icon}
返回
Data quality-oriented scan planning for steel structure scenes using a probabilistic genetic algorithm
.png){kind=icon}
PRE
AI
DOI：10.1016/j.autcon.2024.105700.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Scan planning is often challenging particularly in steel structure scenes because of its complex shapes and occlusions. Meeting the requirements of data quality for the scan-to-BIM model is also another issue for accurate point cloud data acquisition. To address these issues, this study proposes a solution that determines an optimal number of scans and corresponding scan positions and parameters. Three primary steps include 1) extraction of feature points using a slicing cutting method and range images, 2) evaluation of data quality using visibility check and data density evaluation, and 3) determination of optimal scan configuration using a probabilistic genetic algorithm. In order to validate the proposed solution, a series of lab-scale experiments involving five case studies with different scenarios are conducted and the results show a similarity of 88.4% between simulation and actual experiments, demonstrating the feasibility of the proposed method for steel structure scenes with complex shapes and occlusions.
Keyword:
Scan planning
Steel structure scenes
Data quality
Data acquisition
Scan-to-BIM
Probabilistic genetic algorithm
期刊
IF:
11.5
论文数:
6.3K
被引数:
4.2W
机构
引用论文
Geometric Properties of Steel Components with Stability and Fatigue Risks Using 3D-Laser-Scanning.png)
BUILDINGS
OAAI
BUILDINGSIF3.1
OAAI分享收藏Semi-automated generation of parametric BIM for steel structures based on terrestrial laser scanning data基于地面激光扫描数据的钢结构参数化BIM半自动生成
PREAI
分享收藏Point cloud quality requirements for Scan-vs-SIM based automated construction progress monitoring基于scan-vs-sim的自动化施工进度监控的点云质量要求
PREAI
分享收藏Automated vision-based construction progress monitoring in built environment through digital twin
OAAI
OAAI分享收藏
查看更多内容